Combating misinformation in the digital age has been a challenging subject with significant social implications, as misinformation continues to shape public responses to elections, pandemics, and other critical events. Despite extensive research efforts, traditional mitigation approaches have fallen short, largely due to their narrow focus on detection accuracy while overlooking the social and emotional influences that drive user behavior. This article proposes to frame information belief as a social norm, enabling us to track and predict its evolution at the individual user level. By operating on an individual level, we can identify and target specific users susceptible to adopting certain norms ahead of time, providing a more powerful approach to mitigating the effects of misinformation. To accomplish this, we develop a temporal graph neural network equipped with projection operators capable of forecasting arbitrary time horizons. The network is trained to predict individual users’ tipping points—the moments when they transition from resistance to acceptance of particular information norms. We conduct rigorous analysis of the temporal smoothness of our predictions, leading to an algorithm that queries the model to identify critical moments before users reach their tipping points, enabling preemptive action. To our knowledge, this represents the first successful attempt at predicting individual tipping points in real-world information networks, marking a significant advance in our ability to address misinformation through preventive rather than reactive measures.

We study the Quality of Service Degradation (QoSD) problem, in which an adversary perturbs edge weights to degrade network performance. This setting arises in both network infrastructures and distributed ML systems, where communication quality, not just connectivity, determines functionality. While classical methods rely on combinatorial optimization, and recent ML approaches address only restricted linear variants with small-size networks, no prior model directly tackles the QoSD problem under nonlinear edge-weight functions. This work proposes Hephaestus, a self-reinforcing generative framework that synthesizes feasible solutions in latent space, to fill this gap. Our method includes three phases: (1) Forge: a Predictive Path-Stressing (PPS) algorithm that uses graph learning and approximation to produce feasible solutions with performance guarantee, (2) Morph: a new theoretically grounded training paradigm for Mixture of Conditional VAEs guided by an energy-based model to capture solution feature distributions, and (3) Refine: a reinforcement learning agent that explores this space to generate progressively near-optimal solutions using our designed differentiable reward function. Experiments on both synthetic and real-world networks show that our approach consistently outperforms classical and ML baselines, particularly in scenarios with nonlinear cost functions where traditional methods fail to generalize.